An image processing device (e.g., a camera) may include a digital sensor therein that, in turn, may include a number of sensor elements. The limited number of sensor pixels associated with the aforementioned sensor elements may limit the resolution of a digital image captured through the image processing device. Utilizing the digital sensor may result in fine details of the digital image being “blocky,” and, therefore, not resolvable. Increasing dimension(s) of the sensor and/or the sensitivity thereof may result in the image processing device capturing high quality digital images but there may be increased cost(s) associated therewith. Moreover, there may be physical limit(s) associated with sensor manufacturing. For example, large spacing between pixels of the sensor are imposed by the physical limits of thermal imaging systems.
The resolution of a digital image may be enhanced through super-resolution, which may involve fusing a number of low resolution images of a same scene. Motion-based super-resolution may involve the assumption of each low resolution image being different from other low resolution images due to relative scene motion. Moreover, motion-based super-resolution may involve solving inverse problems, whose solutions may be required to be insensitive to model estimation errors and/or to be stable with respect to small errors in captured images. Further, the model involved therein may need to be implementable with reasonable computational power requirements. However, motion estimation method(s) may be difficult, computationally expensive and/or inaccurate, especially when the recorded images have low resolution.
Motionless super-resolution may involve the generation of a super-resolved image by fusing a number of slightly differing low resolution images of the same scene. Here, the difference may be due to varying blurring processes. However, as motionless super-resolution may also involve a model-based approach, the solutions therefrom may also be sensitive to errors therein.